Famous coding problems every developer should know

Famous coding problems every developer should know

In the realm of computer science and software development, coding problems serve as both gatekeepers and trainers. They hone the algorithmic and problem-solving muscles of developers, ensuring that the most foundational principles of the discipline are not only understood but also wielded effectively. Many tech giants and startups alike lean on these problems during interviews, viewing them as accurate measures of a candidate’s logical and algorithmic acumen. Beyond interviews, coding competitions, which attract thousands of participants globally, heavily rely on such problems to test and rank coders.

Basic Data Structures and Algorithms

Understanding foundational data structures and algorithms is like possessing the basic tools in any craft. Without this knowledge, a software developer might find themselves inappropriately equipped to approach or optimize many problems.

Arrays and Strings

Arrays and strings are among the most rudimentary yet powerful data structures. They form the backbone of many solutions, and their versatility is a testament to their significance.

Two Sum

The Two Sum problem is simple yet intriguing: Given an array of integers, find two numbers such that they add up to a specific target number. The typical solution involves using a hash map to store numbers and their indices, allowing for a solution with O(n) time complexity.
For a comprehensive solution and analysis, you can check out the official LeetCode discussion.

Reverse a String/Array

The task is to reverse the elements of a string or an array. This can be achieved by swapping elements symmetrically from the start and end until the middle is reached. This solution works in O(n) time.
Here’s a Python example for an array:

def reverse_array(arr):
left, right = 0, len(arr) - 1
while left < right:
arr[left], arr[right] = arr[right], arr[left]
left, right = left + 1, right - 1
return arr

Longest Substring Without Repeating Characters

The challenge here is to find the length of the longest substring without repeating characters. A sliding window approach works wonders for this problem. By using two pointers and a set to track unique characters, we can achieve a solution with O(n) time complexity.

Linked Lists

Linked Lists offer a dynamic way to store data, where each element points to the next, allowing for efficient insertions and deletions.

Detect a Cycle in a Linked List

To determine if a linked list has a cycle, Floyd’s cycle-finding algorithm, often termed as the “tortoise and the hare” approach, is highly effective. In this method, two pointers move through the list at different speeds. If there’s a loop, they’re bound to meet.

Merge Two Sorted Lists

Given two sorted linked lists, the task is to merge them into one sorted list. This can be achieved both recursively and iteratively. The recursive solution is intuitive, comparing the current nodes of both lists and choosing the smaller one.
The iterative approach involves using a dummy node and a current pointer to stitch together the merged list.

Reverse a Linked List

Reversing a linked list can be done iteratively by redirecting pointers or recursively by backtracking and reassigning links. Both methods have their own charm and elegance.

2.3. Trees and Graphs

Trees and graphs capture hierarchical and relational data, respectively, enabling solutions to many complex problems.

Binary Tree Inorder/Preorder/Postorder Traversal

Traversal in binary trees can be done recursively or iteratively using a stack. Each order (in-order, pre-order, post-order) has its own sequence of visiting the root, left child, and right child.

Check if a Tree is Balanced

A balanced tree is one where the difference in height between the left and right subtrees of any node is not more than one. By recursively checking the balance of each subtree and tracking the height, we can determine if a tree is balanced in O(n) time.

Advanced Data Structures and Algorithms

As developers advance in their journey, more sophisticated structures and algorithms emerge on the horizon, offering powerful solutions to intricate problems.

Heaps and Queues

Heaps are specialized tree structures that maintain a specific order, while queues, especially deques, offer a double-ended approach to add or remove data.

Kth Largest Element in an Array

By leveraging a min-heap or max-heap, one can efficiently find the kth largest element in an array. By pushing values into the heap and popping them off, the desired order can be achieved.

Sliding Window Maximum

To find the maximum for each possible subarray of size k, a deque can be employed. This ensures that the deque’s front always has the current window’s maximum value.

Dynamic Programming

Dynamic programming (DP) is a powerful technique that enables developers to solve complex problems by breaking them into smaller subproblems. Once these subproblems are solved, their solutions can be stored and reused, preventing redundant computations and often resulting in highly optimized algorithms.

Coin Change Problem

The coin change problem is a classic DP challenge. Imagine you are given an array of coin denominations and a total amount. The objective is to determine the minimum number of coins that you need to make up that amount. If the amount cannot be made up by any combination of the coins, return -1.

Solution: One can utilize a bottom-up approach. Create an array to store the minimum number of coins needed for each amount from 0 to the target amount. Initialize this array with a large value, say INT_MAX, and iterate over each denomination for each amount. Update the array by comparing the current value and 1 plus the value at the current amount minus the denomination.

Longest Common Subsequence

This problem asks to find the length of the longest subsequence common to two sequences. It’s important to note that a subsequence doesn’t require elements to be contiguous.

Solution: Use a 2D table where entry at the ith and jth index represents the length of LCS of the two sequences up to their ith and jth character.

Knapsack Problem

The 0/1 Knapsack problem entails a set of items, each with a weight and a value, and a knapsack with a maximum capacity. The task is to determine the maximum value you can achieve by placing items in the knapsack without exceeding its capacity.

Solution: Similar to LCS, a 2D DP table can be employed. The table’s value at any cell will be the maximum of two scenarios: including the item or excluding it.

Trie and Hashing

Trie, a tree-like data structure, is beneficial for word searches in dictionaries. Hashing, on the other hand, is instrumental for quick data retrieval using keys.

Word Search II

This problem requires identifying all words from a given list that can be formed in a grid of characters.

Solution: A Trie can be constructed from the word list. DFS search on the grid will then efficiently find words in the Trie.

Group Anagrams

Given an array of strings, this task groups anagrams together.

Solution: A hash map can be used where the sorted version of the word is the key and the original words are the values.

Searching and Sorting

Searching and sorting are fundamental operations in computer science. Mastering efficient algorithms for these tasks is crucial for any developer.

Binary Search

Binary search is used on sorted arrays. The idea is to divide the search interval in half repetitively.

Solution: If the target is equal to the middle element, you’ve found it. If it’s less, then search the left half. Otherwise, search the right half.

Merge Sort & Quick Sort

These are divide and conquer sorting algorithms. Merge sort divides the array into two halves, sorts them, and merges them. Quick sort partitions an array and then recursively sorts the partitions.

Topological Sort

Topological sorting orders directed acyclic graphs (DAGs) in a linear fashion.

Solution: A Depth First Search (DFS) can be applied to resolve this.

Bit Manipulation

Bitwise operations operate at the level of individual bits. These operations are both intriguing and handy for certain algorithms.

Single Number

In an array where every element appears twice except for one, the challenge is to find that single one.

Solution: Using XOR operation on all elements, the result will be the element appearing only once.

Counting Bits

Count the number of ones in the binary representation of numbers from 0 to n.

Solution: Iterate from 0 to n, and for each number, use bit manipulation to count the ones.

Design Problems

Design problems test one’s ability to structure systems and algorithms, a skill essential for senior developers and architects.

Design a LRU Cache

LRU (Least Recently Used) Cache is a type of cache that removes the least recently used entry when the cache reaches its limit.

Solution: A combination of hash map and doubly linked list ensures O(1) time complexity for both get and put operations.

Design TinyURL

URL shortening services are popular, and understanding the design considerations is fundamental.

Solution: Hashing can be applied to generate a unique short URL. Additionally, a database can store the mapping between the short and long URLs.

Math and Logic Puzzles

These problems hone one’s analytical skills, demanding more than just algorithmic solutions.

Sudoku Solver

Sudoku is a popular game where one needs to fill numbers from 1 to 9 in a grid.

Solution: A backtracking algorithm is apt for this, attempting to place numbers and backtracking if a solution isn’t feasible.

Valid Number

Determine if a given string represents a valid number – be it integer, decimal, or in exponential format.

Solution: Finite state machines can be applied. The string can be parsed character by character to decide if it forms a valid number.


Being well-acquainted with these problems will not only boost your coding skills but also your problem-solving prowess. It’s essential to practice continuously, study the solutions, and understand the underlying principles. Platforms like codedamn offer a myriad of challenges to hone these skills.

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